Gold Bhasma and Silver Parpam Used in Indian Traditional Medicines. Scientific Validation of their Interaction with Human Cells
Bibliographic record
Abstract
Bhasmas in Ayurveda and parpams in Siddha medicine are unique herbo-metallic/mineral preparations, effective remedies, fabricated from highly purified metals, treated with a variety of herbal decoctions and incinerated at high temperatures to, finally, obtain a metal ash, having a significantly reduced size and free of toxic effects. The processing techniques of bhasmas and their use as medicines have been described in ancient texts of Ayurveda such as Rasa Shastra, Charaka Samitha and Sushruta Sambita. It has been emphasized that, while Siddha medicine is close to Ayurveda, Siddha has been closely linked to the Tantric religious movement, traced back to the 6th century AD and it is believed that Alchemy played a more central role in Siddha medicine than in Ayurveda. Some of the most important bhasmas and parpams are briefly described; their fabrication and properties are mentioned. The study of the interaction of gold bhasma and silver parpam with human cells investigated by our group is described in the second part of this work. In this section, the cellular uptake and localization of the gold and silver particles in cancerous and normal cells have been elucidated by using, principally, the hyperspectral imaging method that combines the image with the spectral information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".